Instructions to use wyt2000/InverseCoder-CL-7B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use wyt2000/InverseCoder-CL-7B with Transformers:

# Use a pipeline as a high-level helper
from transformers import pipeline

pipe = pipeline("text-generation", model="wyt2000/InverseCoder-CL-7B")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("wyt2000/InverseCoder-CL-7B")
model = AutoModelForCausalLM.from_pretrained("wyt2000/InverseCoder-CL-7B")
messages = [
    {"role": "user", "content": "Who are you?"},
]
inputs = tokenizer.apply_chat_template(
	messages,
	add_generation_prompt=True,
	tokenize=True,
	return_dict=True,
	return_tensors="pt",
).to(model.device)

outputs = model.generate(**inputs, max_new_tokens=40)
print(tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use wyt2000/InverseCoder-CL-7B with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "wyt2000/InverseCoder-CL-7B"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "wyt2000/InverseCoder-CL-7B",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/wyt2000/InverseCoder-CL-7B

SGLang

How to use wyt2000/InverseCoder-CL-7B with SGLang:

Install from pip and serve model

# Install SGLang from pip:
pip install sglang
# Start the SGLang server:
python3 -m sglang.launch_server \
    --model-path "wyt2000/InverseCoder-CL-7B" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "wyt2000/InverseCoder-CL-7B",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker images

docker run --gpus all \
    --shm-size 32g \
    -p 30000:30000 \
    -v ~/.cache/huggingface:/root/.cache/huggingface \
    --env "HF_TOKEN=<secret>" \
    --ipc=host \
    lmsysorg/sglang:latest \
    python3 -m sglang.launch_server \
        --model-path "wyt2000/InverseCoder-CL-7B" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "wyt2000/InverseCoder-CL-7B",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use wyt2000/InverseCoder-CL-7B with Docker Model Runner:
```
docker model run hf.co/wyt2000/InverseCoder-CL-7B
```

wyt2000 commited on Jul 8, 2024

Commit

fd79d2c

2 Parent(s): 1a33343 0b7fd94

Merge branch 'main' of https://huggingface.co/wyt2000/InverseCoder-CL-7B

Browse files

Files changed (1) hide show

README.md +146 -5

README.md CHANGED Viewed

@@ -1,5 +1,146 @@
----
-license: llama2
-license_name: deepseek
-license_link: LICENSE
----

+---
+license: llama2
+license_name: deepseek
+license_link: LICENSE
+datasets:
+- ise-uiuc/Magicoder-Evol-Instruct-110K
+library_name: transformers
+pipeline_tag: text-generation
+tags:
+- code
+model-index:
+  - name: InverseCoder-CL-7B
+    results:
+      - task:
+          type: text-generation
+        dataset:
+          type: openai_humaneval
+          name: HumanEval
+        metrics:
+          - name: pass@1
+            type: pass@1
+            value: 0.762
+            verified: false
+      - task:
+          type: text-generation
+        dataset:
+          type: openai_humaneval
+          name: HumanEval(+)
+        metrics:
+          - name: pass@1
+            type: pass@1
+            value: 0.720
+            verified: false
+      - task:
+          type: text-generation
+        dataset:
+          type: mbpp
+          name: MBPP
+        metrics:
+          - name: pass@1
+            type: pass@1
+            value: 0.706
+            verified: false
+      - task:
+          type: text-generation
+        dataset:
+          type: mbpp
+          name: MBPP(+)
+        metrics:
+          - name: pass@1
+            type: pass@1
+            value: 0.601
+            verified: false
+      - task:
+          type: text-generation
+        dataset:
+          type: ds1000
+          name: DS-1000 (Overall Completion)
+        metrics:
+          - name: pass@1
+            type: pass@1
+            value: 0.399
+            verified: false
+      - task:
+          type: text-generation
+        dataset:
+          type: nuprl/MultiPL-E
+          name: MultiPL-HumanEval (Java)
+        metrics:
+          - name: pass@1
+            type: pass@1
+            value: 0.487
+            verified: false
+      - task:
+          type: text-generation
+        dataset:
+          type: nuprl/MultiPL-E
+          name: MultiPL-HumanEval (JavaScript)
+        metrics:
+          - name: pass@1
+            type: pass@1
+            value: 0.619
+            verified: false
+      - task:
+          type: text-generation
+        dataset:
+          type: nuprl/MultiPL-E
+          name: MultiPL-HumanEval (C++)
+        metrics:
+          - name: pass@1
+            type: pass@1
+            value: 0.526
+            verified: false
+      - task:
+          type: text-generation
+        dataset:
+          type: nuprl/MultiPL-E
+          name: MultiPL-HumanEval (PHP)
+        metrics:
+          - name: pass@1
+            type: pass@1
+            value: 0.552
+            verified: false
+      - task:
+          type: text-generation
+        dataset:
+          type: nuprl/MultiPL-E
+          name: MultiPL-HumanEval (Swift)
+        metrics:
+          - name: pass@1
+            type: pass@1
+            value: 0.530
+            verified: false
+      - task:
+          type: text-generation
+        dataset:
+          type: nuprl/MultiPL-E
+          name: MultiPL-HumanEval (Rust)
+        metrics:
+          - name: pass@1
+            type: pass@1
+            value: 0.461
+            verified: false
+      - task:
+          type: text-generation
+        dataset:
+          type: nuprl/MultiPL-E
+          name: MultiPL-HumanEval (Average for non-python languages)
+        metrics:
+          - name: pass@1
+            type: pass@1
+            value: 0.529
+            verified: false
+---